Anjani Kumar
Principal Data Scientist • Amazon • Cloud & MLOps Expert
About Me
As a Principal Data Scientist at Amazon, I specialize in architecting and deploying machine learning systems on cloud platforms. With over 5 years of experience, my core focus is on the operational side of data science—ensuring models are not just accurate, but also scalable, maintainable, and integrated into production environments using best-in-class MLOps practices.
I believe that the true value of a data scientist is their ability to ship working, automated systems. My expertise lies in Azure ML, CI/CD pipelines for machine learning, and building data platforms that serve as the backbone for intelligent applications.
Areas of Expertise
Production ML Systems
Building and maintaining machine learning models that operate reliably and efficiently at scale.
Cloud Platforms (Azure, AWS)
Leveraging cloud infrastructure to build serverless data pipelines, training jobs, and API endpoints.
MLOps & CI/CD Pipelines
Automating the machine learning lifecycle, from data validation and training to deployment and monitoring.
Team Leadership
Leading teams of data scientists and engineers to deliver complex, large-scale analytics projects.
Proof: Published Works & Projects
- Professional Profile: Anjani Kumar on LinkedIn — Details my experience at Amazon and other leading tech companies.
- GitHub Portfolio (Example): MLOps Pipeline Project — A sample repository showcasing automated model deployment. [Replace with real repo]
- Cloud Certification (Example): Azure Data Scientist Associate — Verifiable expertise in Microsoft's cloud machine learning platform. [Replace with real link]
My Review Process & Audit Details
Cloud Project Validation
I examine if projects are designed for the cloud, utilizing
services like serverless functions, object storage, and container
registries, not just local notebooks.
For example,
AWS Machine Learning projects
and
Google Cloud AI case studies
demonstrate real-world cloud deployments.
MLOps Rigor
I check for signs of automation and engineering discipline: Does the course teach version control for data (DVC), experiment tracking (MLflow), and CI/CD for models (Google Vertex AI MLOps)?
Score Contribution
| Course | Cloud Integration | MLOps Practices | Deployment | Portfolio Value |
|---|---|---|---|---|
| Course C | 0.0 / 5.0 | 0.0 / 5.0 | 0.0 / 5.0 | 0.0 / 5.0 |
| Notes: Excellent MLOps coverage with a fully automated CI/CD pipeline project. Strong Azure focus. | ||||
| Course D | 0.0 / 5.0 | 0.0 / 5.0 | 0.0 / 5.0 | 0.0 / 5.0 |
| Notes: Teaches Docker and API deployment, but lacks automated pipelines and cloud integration. | ||||
Final rankings are an average across all reviewers. See the full scoring rubric.
What I Look For: My Evaluation Philosophy
Cloud-Native Approach
Projects should go beyond local `localhost` servers and
demonstrate how to leverage scalable, managed cloud services.
Proof:
cloudnative
Deployable Artifacts
I value courses that teach students to produce container images,
infrastructure-as-code scripts, and API specifications.
Proof:
businessworks
Automation Mindset
The best programs teach students to automate everything: data
validation, training, deployment, and monitoring.
Proof:
Workato
Production-Ready Skills
I look for skills that are directly transferable to a job, such as experiment tracking, model monitoring, and A/B testing frameworks.
My Advice to Aspiring Data Scientists
“A model in a notebook is an experiment. A model served via a scalable, monitored API is a product. Strive to be the person who can build the product, not just run the experiment. That is the key to a successful career in this field.”
Transparency & Updates
Conflict of Interest & Independence
My evaluations are independent and based on technical merit. I have no financial relationship with any of the course providers listed.
Latest Updates & Corrections
Published: 15 September 2025
Last Reviewed: 15 September 2025
If you find an error, please
contact me.
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